2. Artificial Neural Network in Microwave Cavity Filter Tuning

  1. Apostolos Georgiadis2,
  2. Hendrik Rogier3,
  3. Luca Roselli4 and
  4. Paolo Arcioni5
  1. Jerzy Julian Michalski,
  2. Jacek Gulgowski,
  3. Tomasz Kacmajor and
  4. Mateusz Mazur

Published Online: 20 SEP 2012

DOI: 10.1002/9781118405864.ch2

Microwave and Millimeter Wave Circuits and Systems: Emerging Design, Technologies, and Applications

Microwave and Millimeter Wave Circuits and Systems: Emerging Design, Technologies, and Applications

How to Cite

Michalski, J. J., Gulgowski, J., Kacmajor, T. and Mazur, M. (2012) Artificial Neural Network in Microwave Cavity Filter Tuning, in Microwave and Millimeter Wave Circuits and Systems: Emerging Design, Technologies, and Applications (eds A. Georgiadis, H. Rogier, L. Roselli and P. Arcioni), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118405864.ch2

Editor Information

  1. 2

    CTTC, Spain

  2. 3

    Ghent University, Belgium

  3. 4

    University of Perugia, Italy

  4. 5

    University of Pavia, Italy

Author Information

  1. TeleMobile Electronics Ltd, Pomeranian Science and Technology Park, Gdynia, Poland

Publication History

  1. Published Online: 20 SEP 2012
  2. Published Print: 26 OCT 2012

ISBN Information

Print ISBN: 9781119944942

Online ISBN: 9781118405864

SEARCH

Keywords:

  • Microwave filter;
  • Artificial Neural Network;
  • Passive Intermodulation;
  • Sequential filter tuning method;
  • Parallel filter tuning method;
  • Discrete wavelet transform;
  • Principal component analysis (Karhunen-Loeve transformation)

Summary

The second, chapter, Artificial neural network in microwave cavity filter tuning, by Jerzy Michalski, Jacek Gulgowski, Tomasz Kacmajor, and Mateusz Mazur from TeleMobile Electronics Ltd., Gdynia, Poland is related to filter optimization. Presently, microwave filter tuning is a necessary step in the production process. This step typically consists of manual work performed by a trained operator and usually requires considerable amount of time. Hence, there is great expectation among microwave filter production companies to automate the process. Automated methods of filter tuning based on Artificial Neural Networks are suggested and different approaches to the problem are described with a series of experiments supporting the presented ideas.